SandboxAPI ships an MCP server at https://mcp.sandboxapi.dev/mcp. Drop the JSON config below into Claude Desktop, Cursor, VS Code, or any other MCP-compatible client. Your AI can now run code in 8 languages and install packages into existing sessions.
The current MCP surface mirrors the highest-signal agent workflow: execute code, batch snippets, discover runtimes, inspect capabilities, and install packages into sessions.
Pick your client, paste the JSON, replace YOUR_API_KEY with your RapidAPI key. Done.
Edit the Claude Desktop config and restart the app.
{
"mcpServers": {
"sandboxapi": {
"url": "https://mcp.sandboxapi.dev/mcp",
"headers": {
"Authorization": "Bearer YOUR_API_KEY"
}
}
}
}
~/Library/Application Support/Claude/claude_desktop_config.json
Add to your Cursor MCP config (Settings → MCP).
{
"mcpServers": {
"sandboxapi": {
"url": "https://mcp.sandboxapi.dev/mcp",
"headers": {
"Authorization": "Bearer YOUR_API_KEY"
}
}
}
}
Settings → MCP → Add server
Compatible with the Continue and Cline MCP integrations.
{
"mcpServers": {
"sandboxapi": {
"url": "https://mcp.sandboxapi.dev/mcp",
"headers": {
"Authorization": "Bearer YOUR_API_KEY"
}
}
}
}
~/.continue/config.json or via the extension UI
An agent in Claude Desktop runs code, installs pandas into an existing session when needed, and reports findings without leaving the chat.
The MCP integration is the fastest way to ship one of these primitives in your AI agent product.
Give your AI safe Python execution with file-backed state. The agent can pip install into an existing session and keep working with generated files.
Failing test? The agent runs the code, sees the traceback, edits the file, runs again. Tight loops via stateful sessions instead of restart-and-pray.
Build a lightweight code execution tool on your own stack and model. 8 languages, gVisor isolation, and a small MCP surface agents can reason about.
Get an API key, paste the JSON config, and your AI is running code in 30 seconds.